Procedure for extracting information from a heart sound signal

ABSTRACT

The invention relates to a procedure for extracting features from phonocardiographic signals without the use of synchronizing information from electrocardiographic signals. The features extracted are the timing and value of first and second heart sounds and various combinations of timing and value of signal components constituting heart murmur. Such combinations are directly related to various heart conditions, which are more easily diagnoseable by a medically trained person when assisted by the signal extraction according to the invention. The features are extracted by a novel combination of energy/time relationships for the heart signal and various novel classification schemes.

The invention relates to a procedure for extracting information from aphonocardiographic signal obtained from a transducer and subjected tosignal processing in order to aid evaluation and diagnosis of heartconditions. The invention furthermore relates to techniques forming partof such extraction and apparatus to perform such feature extraction aswell as coding the features to aid the ability to distinguish betweenrelated features.

Signals obtained by means of a transducer are phonocardiographicrepresentations of sounds traditionally listened to by means of astethoscope. Training in auscultation takes a long time and requires anaptitude for recognising and classifying aural cues, frequently in anoisy environment. 20-30 different conditions may need to bedifferentiated, and within each, the severity evaluated. Furthermore,there may be combinations among these. These factors contribute toexplaining why not all physicians perform equally well when diagnosingheart conditions, and why it may be time-consuming.

The so-called first (S1) and second (S2) heart sound are very importantmarkers in the assessment of a heart sound signal. These sounds aredirectly related to the functioning of the heart valves, in that S1 iscaused by the closure of the atrioventricular valves and contraction ofthe ventricles and S2 is caused by the closure of the aortic andpulmonary valves.

It has been tried to use further signals derived from ECG signals todetermine the points in time during which to expect specific heartsounds, such as U.S. Pat. No. 5,685,317, and as described inHaghighi-Mood, A. et al. “A sub-band energy tracking algorithm for heartsound segmentation”, In: Computers in Cardiology 1995, Vienna, Austria,10-13 Sep. 1995, pp. 501-504, which latter is model-based (AR). Theextra complication of using ECG in addition to phonocardiographicsignals is not generally desirable.

A number of patents relate to the extraction of the S1 and S2 signals,such as U.S. Pat. No. 6,048,319, which concerns the measurement of thetime interval between the S1 and S2 signals in relation to the heartrate in order to determine the degree of coronary artery disease. Themeasurement is based on peak detection and autocorrelation and it may beconsidered a relatively slow process.

In order to determine the occurrence of the first and second heartsounds a wavelet analysis and re-synthesis and various time occurrencemanipulations are used in Huiying, L. et al. “A heart sound segmentationalgorithm using wavelet decomposition and reconstruction”, ENGINEERINGIN MEDICINE AND BIOLOGY SOCIETY, 1997. PROCEEDINGS OF THE 19TH ANNUALINTERNATIONAL CONFERENCE OF THE IEEE, Chicago, Ill., USA, 30 Oct.-2 Nov.1997, Vol. 4, pp. 1630-1633. It is described as being a good basis forfurther analysis of heart sound signals.

In WO 02/096293 Ala complex procedure is described, comprising the useof wavelets, calculating the signal's Shannon's energy, calculating thearea of each of a number of energy envelopes, and performing clusteranalysis. The latter is needed to identify the S1 and S2 signals, but itis a complicated procedure, and the output of the complex procedure is anumber of diagnoses, including murmur.

A different category of signals related to various heart conditions isgenerally known as murmurs. The known procedures of isolating andcategorizing murmurs are generally dependent on the simultaneousrecording of electrocardiographic data, such as U.S. Pat. No. 5,957,866and U.S. Pat. No. 6,050,950 and this complicates the practical use ofauscultation techniques considerably.

The above solutions are very complex and rely on techniques that areequivalent to a long averaging time. According to the invention a methodhas been derived which is more precise and obtains a faster result. Thisis obtained by a sequence of steps, comprising an optional adaptivenoise reduction, detection of S1 and S2, e.g. by means of the featureextraction procedure mentioned above, enhancement of the signal byelimination of the S1 and S2 contributions, performing spectral analysisand feature enhancement in order to obtain the energy content present inareas of a time-frequency representation delimited by frequency bandtimes time interval in the form of energy distributions, classifying theenergy distributions according to pre-defined criteria, and comparingthe energy distributions to a catalogue of distributions related toknown medical conditions and extracting information by comparing theenhanced signal to stored time functions.

According to the present invention the detection of S1 and S2 isobtained by performing the steps of feature extraction andclassification based on the energy distribution over time in a featuretime function. The feature extraction is performed by the steps ofbandpass filtering, followed by instantaneous power and lowpassfiltering. This generates a series of signal peaks or “hills”, eachrelating to either an S1 or an S2, and a signal classification stepdetermines which “hill” is to be regarded as either an S1 or an S2,whereby a systole is correctly identified.

The correct placement in time of S1 and S2 permits the energy relatingto these sounds to be eliminated in the signal processing, and theresulting sound (including murmurs, etc.) is a useful starting signalfor further analysis, because it increases the dynamic range of theremaining signal. It also permits presenting the remaining signal to theears with a superposition of correctly placed but “neutral” S1 and S2contributions as mere time markers, but without any signal that thelistener needs to process in the listening process.

Diagnostic classification and evaluation is obtained by identifyingspecific features in order to extract characteristic patterns which arecompared to a library of patterns typical of various kinds of heartdisorder, and the closeness of the measured signal to these patterns.

Enhanced appreciation and identification of the heart sound features isobtained by “placing” the extracted features in a synthetic acousticenvironment relying on supplying different signals to the ears of alistener by means of headphones. This is obtained by means of so-calledHead Related Transfer Functions, or HRTF.

A specific procedure of the invention is characteristic in that firstand second heart sounds are detected and placed correctly on a time axisby performing the steps of feature extraction and classification basedon the energy distribution over time in a feature time function by thesteps of bandpass filtering, followed by instantaneous power and lowpassfiltering of the original phonocardiographic signal.

An embodiment of the invention is particular in that it comprises thesteps of extracting the first and second heart sounds by classificationaccording to energy levels, eliminating the contribution of the saidfirst and second heart sounds from the signal, performing spectralanalysis and feature enhancement in order to obtain the energy contentpresent in areas of a time-frequency representation delimited byfrequency band times time interval in the form of energy distributions,classifying the energy distributions according to pre-defined criteriacomparing the energy distributions to a catalogue of distributionsrelated to known medical conditions.

A further advantageous embodiment of the invention for extracting murmurinformation is particular in that it comprises the steps of obtaining adigital representation of heart sound for a predetermined number ofseconds, identifying the time of occurence of the first and second heartsounds in each cycle, windowing the parts of heart sounds fallingbetween the first and second heart sounds, and second and first heartsounds, respectively; decomposition of the signals into a predeterminedfirst number n1 of frequency bands, each band being decomposed into apredetermined second number n2 of time-slices, obtaining a systole (SP)and a diastole (DP) power vector consisting of the sum of n1 powersmeasured in each of the n2 time slices, for each combination of afrequency band and a time slice, the power values from the differentsystoles being compared, and the median value being chosen to be thestandard value for a power vector, obtaining a systole (SMF) and adiastole (DMF) mean frequency vector by weighting the power value foreach of n1 frequency bands with the mean frequency of the correspondingband, summing the results and dividing the sum by the correspondingelement in the respective systole or diastole power vector, while usingthe time of occurence of the intensity vectors of the various classesfor classifying the time distribution of murmurs.

A further embodiment of the invention is particular in that it comprisesa step preceding the step of obtaining systole and diastole murmurintensity vectors S1 and DI, namely refining the windowing by settingthe values of SP, DP, SMF, and DMF of the first or last elements equalto the second or last-but-one values, respectively, if the values of thefirst or last elements of the corresponding vectors fulfil predetermineddeviation criteria.

A further embodiment of the invention is particular in that stillfurther steps are included, namely subjecting the signal to doubledifferentiation before decomposition, obtaining a systole (SI) anddiastole (DI) murmur intensity vector, respectively, by taking thelogarithm of the corresponding SP and DP vectors, classifying theobtained logarithmic vectors into murmur intensity classes, andcomparing the energy distributions to a catalogue of distributionsrelated to known medical conditions.

An apparatus for performing the basic procedure of the invention isparticular in that it comprises analog-to-digital means for converting aheart sound signal into sampled data, means for extracting the first andsecond heart sounds by classification according to energy levels, meansfor eliminating the contribution of the said first and second heartsounds from the signal, means for performing spectral analysis, meansfor performing feature enhancement, and multiplication means forobtaining the energy content present in areas of a time-frequencyrepresentation delimited by frequency band multiplied by time intervalin the form of energy distributions means for classifying the energydistributions according to pre-defined criteria, and comparator meansfor comparing the energy distributions to a catalogue of distributionsrelated to known medical conditions.

An embodiment of the inventive apparatus is particular in that signalprocessing means are used to produce a spatial sound distribution basedon frequency, a low frequency band being delivered to a first earpieceof a headphone and a high frequency band being delivered to a secondearpiece of said headphone, the frequency bands containing first andsecond heart sounds and murmur sounds respectively.

A further embodiment of the apparatus is particular in that said signalprocessing means produce a temporal sound distribution, sound signalsbeing first delivered to a first earpiece of the headphone and thenbeing delivered to a second earpiece of the headphone.

A further embodiment of the apparatus is particular in that said signalprocessing means comprise at least one Wiener filter.

The invention will be more fully described in the following withreference to the drawing, in which

FIG. 1 shows a functional block diagram of the complete informationextraction process according to the invention,

FIG. 2 shows the structure providing the first analysis of the heartsignal,

FIG. 3 shows an original heart signal and its corresponding spectrogram,

FIG. 4 shows the spectrogram of a bandpass filtered heart signal,

FIG. 5 shows the result of a time marginal distribution of the energy,

FIG. 6 shows the identification of large and small “hills” in thefeature,

FIG. 7 shows the identification of S1 and S2 irrespective of relativepower,

FIGS. 8(a) and 8(b) are representations of the Wiener scenario asapplied to synthesis and analysis, respectively, of filters for rightand left ears for stereo headphones according to the invention,

FIGS. 9 & 10 are representations, respectively, of a one and a two bandapproach to the application of the Wiener scenario to the stereoheadphones according to the invention,

FIG. 11 is a representation of an alternative manner of presenting heartsounds to right and left ears with the stereo headphones according tothe invention, and

FIG. 12 is a modified embodiment for spatially distributing heartsounds.

In FIG. 1 is seen a functional block diagram of an embodiment of theprocedure and sub-procedures according to the invention. The followingdescription relates to a practical example of an embodiment according tothe invention.

EXAMPLE 1

The input for the procedure consists of 8 seconds of heart sound signal,sampled at a rate of 1000 Hz and read into a digital register subsequentto A/D conversion. The procedure is described with reference to moderndigital technology, however in principle the various classification andsorting of time intervals and levels may be performed by analogue meanson DC voltages and traditional gates.

The detector for S1 and S2 essentially consists of two separateprocesses, a feature extraction part and a classification part. Thepurpose of the feature extraction is to transform the input signal to adomain, in which the respective location in time of S1 and S2 is moredistinct than in the original signal. The classification part determinesthe precise location of S1 and S2 and correctly identifies them as such.

In FIG. 2 is demonstrated how murmurs may be observed in the spectrogramof a time function of an original heart sound. The spectrogram isobtained by Fast Fourier Transform. The first and second heart sounds S1and S2 have only a low-frequency content compared to the broad-bandnature of the murmurs, and for this reason the signal is band-passfiltered by convolution of the original signal with the impulse responsefunction of a bandpass filter. The corresponding spectrogram is shown inFIG. 3, in which peaks of higher energy are visible but not clearlyidentifiable. In order to obtain a time function of the occurence ofthese higher energies, the time marginal distribution of the spectrogramis performed according to Eq. (1):${f(t)} = {\frac{1}{2\pi}{\int{{{\int{{\overset{\_}{x}(\tau)}{g\left( {t - \tau} \right)}{\mathbb{e}}^{- {j\omega\tau}}{\mathbb{d}\tau}}}}^{2}{\mathbb{d}\omega}}}}$

Hereby a “final feature” is obtained as a time function as shown in FIG.5. In essence, this time function is obtained by bandpass filtering,instantaneous power extraction and lowpass filtering. It is now clearthat the “final feature” displays a “hill” every time an S1 or S2 occursin the heart signal.

As the magnitudes of the “hills” corresponding to S1 and S2 arecomparable, it is necessary to distinguish between them by applyingclassification rules. First all “hills” in the “final feature” must beidentified. This is obtained for all samples of the time function whichfulfil the following criteria:feature(k−1)<feature (k) and feature(k)>feature (k+1).

The next step is to construct a table of possible systoles. A systole isa pair of “hills” (S1 and S2) constrained by the time distance betweenthe “hills”. The time distance must fall within the following limits:230 ms<T<500 ms for human hearts.

The final sequences of systoles is determined by finding the sequence ofsystoles in the table having maximum energy that fulfil the followingconstraints:

-   -   systole time deviation <18%    -   time between systoles (diastole)>0.9 times systole time    -   amplitude deviation of S1<500%    -   amplitude deviation of S2<500%    -   in the case of overlapping systoles, the systole with maximum        energy must be selected.

The result of the identification is displayed in FIG. 7, in which a fatblack line to the top of a “hill” indicates the time position of a firstheart sound S1 and a thin black line a second heart sound S2.

With the time positions of the first (S1) and second (S2) heart soundscorrectly detected in the signal (given as sample numbers, correspondingto positions measured in milliseconds) it is now possible to evaluatethe much weaker sounds, the heart murmurs. In the following, thesedetected time positions will be referred to as S1 markers and S2markers, respectively. Reference is again made to FIG. 1.

Delimitation of Systoles and Diastoles.

Only the systole and diastole parts of the heart sound signal are usedfor the murmur detection. All periods, beginning 50 milliseconds afteran S1 marker and ending 50 milliseconds before the immediately followingS2 marker, are defined as systoles.

Correspondingly, all periods, beginning 50 milliseconds after an S2marker and ending 50 milliseconds before the immediately following S1marker, are defined as diastoles. This is a primitive but efficientmanner of eliminating the influence of the very energetic first andsecond heart sounds. At a later stage in the performance of theprocedure some corrections are made (vide below), but it may be moreadvantageous to perform the elimination using more refined approaches atthis early stage in the procedure.

Time and Frequency Decomposition of Systoles and Diastoles.

The sound energy content in the sound signal is calculated by means of aspectrogram based on the Discrete Fourier Transform (DFT) using a vectorlength which is a power of 2, such as 16. In order to be able toclassify murmurs regarding frequency contents and time distribution,each systole and diastole is decomposed into 14 frequency bands and 10time slices, the two lowest frequency bands being discarded. The 14frequency bands cover the frequency range from 62.5 Hz to 500 Hz, eachhaving a width of 31.25 Hz.

Before the calculation of the spectrogram, the sound signal isdifferentiated twice (corresponding to a second order high-passfiltration) in order to take into account the frequency characteristicsof the human hearing, being more sensitive to higher than lowerfrequencies within the frequency range in question.

It is considered that a parallel bank of band pass filters will performfaster in the present environment.

The 10 time slices for a given systole or diastole all have the samewidth, corresponding to 1/10 of the total length of thesystole/diastole.

The combination of frequency bands and time slices creates a 14×10matrix for each systole/diastole. For each element in these matrices,the energy content is divided by the width of the relevant time slice,thus yielding matrices containing the heart sound power (energy pertime) for the 140 time/frequency elements of each systole/diastole.

Definition of Standard Systoles and Diastoles for Each Frequency Band.

The matrices for each systole are combined to a single 14×10 systole (S)matrix by median filtration: For each combination of a frequency rangeand a time slice, the power values from the different systoles arecompared, and the median value is chosen to be the standard value. Thisis an efficient way of obtaining a stable value. Thus, for each of the14 frequency bands (rows in the matrix), 10 standard power valuescombine to a standard systole.

The diastole matrices are combined to a D matrix in the same way.

Extraction of Power and Frequency Feature Vectors.

A systole power (SP) vector with 10 elements is constructed by summingthe 14 standard power values for each of the 10 time slices. Thus, theSP vector consists of the column sums for the S matrix.

A diastole power vector (DP) is constructed in the same way.

A systole mean frequency (SMF) vector (also with 10 elements) iscalculated by weighting the power value for each frequency band with themean frequency of the corresponding band, summing the 14 results, anddividing the sum with the corresponding element in the SP vector.

Correspondingly, a diastole mean frequency (DMF) vector is calculated.

Correction of Feature Vectors for S1/S2 Remnants.

Due to the very simple definition of systoles and diastoles, the firstand/or last tenths of some of the systoles and diastoles may be“contaminated” with parts of S1 or S2. Typically, this is manifested bylarger values of the first/last elements in SP/DP and lower values ofthe corresponding elements in SMF/DMF, because of the high power and therelatively low frequencies of S1 and S2 compared to the murmurs insystoles and diastoles.

Therefore, the beginning and end of the SP, SMF, DP, and DMF vectors areexamined and corrected if necessary in dependence of the followingrelationships:

If SMF(2)−SMF(1)>2*|SMF(3)−SMF(2)| or

SP(1)−SP(2)>2*|SP(2)−SP(3)|,

SP(1)==SP(2) and SMF(1)==SMF(2).

If SMF(9)−SMF(10)>2*|SMF(8)−SMF(9)| or

SP(10)−SP(9)>2*SP(9)−SP(8),

SP(10)==SP(9) and SMF(10)==SMF(9).

Corresponding examinations and corrections are performed for DP and DMF.

Creation of Murmur Intensity Vectors.

The elements in a systole intensity (SI) vector is created from theelements in the SP vector in the following way using absolute values:log10(SP(x)) ≦ −1.25 SI(x) = 0 −1.25 < log10(SP(x)) ≦ −0.80 SI(x) = 1−0.80 < log10(SP(x)) ≦ −0.35 SI(x) = 2 −0.35 < log10(SP(x)) ≦ +0.10SI(x) = 3 +0.10 < log10(SP(x)) ≦ +0.55 SI(x) = 4 +0.55 < log10(SP(x)) ≦+1.00 SI(x) = 5 +1.00 < log10(SP(x)) SI(x) = 6

A diastole intensity (DI) vector is constructed in the same way.

It may be relevant to use values relative to e.g. the intensity of S1and/or S2, in which case the logarithmic conversion may use other limitsthan given above.

Correction of Murmur Intentity Vectors for Noise.

In order to correct for transient noise signals, the followingcorrections are performed:

SI(1) is set to 0, if SI(2) is 0

SI(10) is set to 0, if SI(9) is 0

If any element in SI is more than 1 larger than both of its neighbours,the element is set to be equal to the highest of the neighbours.

Similar corrections are performed for DI.

Classification of the Intensity of Murmurs.

The intensities of any systolic and/or diastolic murmur is defined asbeing the maximum value of SI and/or DI, resp.

If the maximum values are both 0, the heart sound is classified ascontaining no murmurs.

Classification of the Time Distribution of Murmurs.

If at least one of the maximum values found is larger than 0, thesystolic and/or diastolic murmurs are classified according to theprofiles of SI and DI, resp.

Any systolic murmur is classified within the first class in the listbelow whose description matches the content of SI:

Systolic ejection murmurs: The values in SI are increasing to a certainpoint and decreasing after that point.

-   -   Steps that are neither in- or decreasing are allowed within the        increasing as well as the decreasing part of the vector.        Early systolic murmurs: The last five values in SI are all 0.        Early-mid systolic murmurs: The last three values in SI are all        0.        Late systolic murmurs: The first five values in SI are all 0.        Mid-late systolic murmurs: The first three values in SI are all        0.        Pansystolic murmurs: The SI vector does not match any of the        above descriptions.

Any diastolic murmur is classified within the first class in the listbelow whose description matches the content of DI:

Decreasing diastolic murmurs: The values in DI are decreasing, but neverincreasing.

-   -   Steps that are neither in- or decreasing are allowed.        Diastolic murmurs with pre-systolic accentuation: DI(9)>DI(8)        and DI(10)>DI(9).        Uniform diastolic murmurs: The DI vector does not match any of        the above descriptions.        Classification of the Frequency Range of Murmurs.

Systolic and diastolic murmur frequencies are classified according tothe frequency band containing the largest power value in the tenth(s) ofthe systole/diastole corresponding to the found maximum values of SI/DI.

If the largest power value is found in one of the two lowest frequencybands (containing frequencies below 125 Hz), the murmur is classified asa low-frequency murmur.

If the largest power value is found in one of the eight highestfrequency bands (containing frequencies above 250 Hz), the murmur isclassified as a high-frequency murmur.

If the none of the above is the case, the murmur is classified as amedium-frequency murmur.

Use of the Murmur Detection Output.

The output from the procedure is either a string describing the foundmurmur(s) or three values for each found murmur coding for theintensity, the time distribution and the frequency range of themurmur(s). The values may either be used for classification or form thecoordinates in a murmur representation

It will be noted that in the above description of a specific embodimentthat apparently arbitrary steps were introduced of doubledifferentiation (second-order highpass filtering) and of applying alogarithmic function in order to obtain intensity values. These stepshave a psychoacoustic foundation related to the hearing of theauscultating person. It is obvious that the classification may wellproceed without these steps, however it has been determined in practicaluse that the classifications obtained by applying these steps arecommensurate with observations made by trained medical staff, and thatthe results thereby obtained are much more directly applicable to theauscultated phenomena at hand. In this way the medical professional willbe much further aided than by mere reading and comparing sets of threevalues.

EXAMPLE 2

The extraction of features may be used in an enhanced manner by creatinga synthetic spatial environment for two ears to listen to viaheadphones. The ability to distinguish more clearly between severalsimultaneously occuring phenomena has been used by jet airplane pilotsto increase separation when listening to several communication channelssimultaneously. The use of special filters for these purposes is knownand research has been conducted to determine the filter effect of thehead (characterized as Head related Transfer Functions, HRTFs) withregard to sound from two microphones based on the distance and source;see, HRTF Measurements of a KEMAR Dummy-Head Microphone, Bill Gardnerand Keith Martin, MIT Media Lab,(HTTP://sound.media.mit.edu/KEMAR/fdoc.txt, Aug. 8, 2000). However, todate, no known attempt has been made to use this ability in connectionwith features extracted from a heart signal and presented as indexmarkers simultaneously with the heart signal itself so as to presentdifferent features to each of the physician's ears.

FIGS. 8(a) & 8(b) show one manner in which filters can be used tospatially distribute the sounds delivered to the physician's ears toreflect the angular difference between sounds received by the left andright ears. In particular, either the signal to both ears can befiltered or the original signal can be preserved and sent to one ear.

To do so, it is necessary to synthesize the difference at each angle,and this can be done by applying the known Wiener scenario, by which anestimate of the optimal filter coefficients for a specified FIR filtercan be arrived at.

In FIGS. 8(a) & 8(b), d_(estimate)(n) designates the impulse responsesignal z⁻¹ to be synthesized for the ear farthest away from the source,and u(n) designates the impulse response for the other ear, which byproper filtering should mimic d_(estimate)(n). By means of a least meansquare algorithm, the coefficients w are adjusted to minimize the errorsignal e(n). These special filters, one for each angle, are used toarbitrarily place a sound spatially when listening via a stereo headset,the original sound being presented to one ear and a filtered version tothe other ear. The listening experience can also be expanded by creationof a synthetic listening space in which low frequency sounds, such asheart beats with the enhancements described in Example 1, are perceivedas coming from, e.g., the left side while high frequency sounds, such asheart murmurs, from the right side. Similarly, earlier phenomena couldbe made to appear on, e.g., the left side and subsequent phenomena onthe right so that, with a repetitive sequence, there would be arepetition of sounds moving from left to right. In these manners,separating and distinguishing of features is facilitated. FIGS. 9 & 10represent one band and two band scenarios, respectively, for achievingthese effects. In FIG. 9, the input sound passes through to the leftear, while the signal to the right ear is processed in one of the abovemanners. In FIG. 10, the input sounds are separated and independentlyprocessed en route to each ear.

FIG. 11 shows an arrangement for transformation of heart sounds fromfrequency distribution to a spatial distribution. The sound signal isfirst divided into a number of frequency bands by normal filters ororthogonal filters, orthogonal filters preventing redundancy, whichensures energy preservation. The output from each filter has a directpath and a delayed path to the matrix circuits for the left and rightchannels. In the matrices, a weighted sum of the input signals is formedin such a way that the lowest to highest frequency bands are perceivedas being spatially distributed from left to right, when played back viaa stereo speaker system or preferably via a stereo headset. In this way,an alternative presentation can be offered which adds a new dimension tothe sound, which apparently enhances the perceived frequency resolution,and by that, the ability to recognize murmurs etc.

FIG. 12, on the other hand, shows an arrangement for the transformationheart sounds from a temporal distribution to a spatial distribution. Inthis embodiment, the spatial location of the sound follows the temporallocation in the heart cycle, from a first heart sound to the next firstheart sound, s1 to s1. This means that the systole, s1 to s2, is locatedon the left side and the diastole, s2 to s1, on the right side. It islike the balance is automatically adjusted with time triggered by asignal derived from the input signal itself. In this way, physicians areoffered an alternative presentation which is meant to help locatemurmurs in the heart cycle, mainly systolic or mainly diastolic.

It will be understood that once the signal has been converted to digitalrepresentation of data, its manipulation may take place in dedicatedprocessors, RISC processors, or general purpose computers, the outcomeof the manipulation being solely dependent on the instructions performedon the data under the control of the program written for the processorin order to obtain the function. The physical location of the data atany one instant (i.e. in varying degrees of processing) may or not berelated to a particular block in the block diagram, but therepresentation of the invention in the form of interconnected functionalblocks provides the skilled person with sufficient information to obtainthe advantages of the invention.

The foregoing description of the specific embodiments will so fullyreveal the general nature of the present invention that others skilledin the art can, by applying current knowledge, readily modify or adaptfor various applications such specific embodiments without undueexperimentation and without departing from the generic concept, andtherefore, such adaptations and modifications should and are intended tobe comprehended within the meaning and range of equivalents of thedisclosed embodiments. It is to be understood that the phraseology orterminology employed herein is for the purpose of description and not oflimitation. The means, materials, and steps for carrying out variousdisclosed functions may take a variety of forms without departing fromthe invention.

Thus, the expressions “means to . . . ” and “means for . . . ”, or anymethod step language, as may be found in the specification above and/orin the claims below, followed by a functional statement, are intended todefine and cover whatever structural, physical, chemical, or electricalelement or structure, or whatever method step, which may now or in thefuture exist which carries out the recited functions, whether or notprecisely equivalent to the embodiment or embodiments disclosed in thespecification above, i.e., other means or steps for carrying out thesame function can be used; and it is intended that such expressions begiven their broadest interpretation.

1. A procedure for extracting information from a phonocardiographicsignal obtained from a transducer and subjected to signal processing,characterised in that first and second heart sounds are detected andplaced correctly on a time axis by performing the steps of featureextraction and classification based on the energy distribution over timein a feature time function by the steps of bandpass filtering, followedby instantaneous power and lowpass filtering of the originalphonocardiographic signal.
 2. A procedure for extracting informationfrom a phonocardiographic signal according to claim 1, includingidentification of characteristic signal components, characterised inthat it comprises the following steps: extracting the first and secondheart sounds by classification according to energy levels, eliminatingthe contribution of the said first and second heart sounds from thesignal, performing spectral analysis and feature enhancement in order toobtain the energy content present in areas of a time-frequencyrepresentation delimited by frequency band times time interval in theform of energy distributions classifying the energy distributionsaccording to pre-defined criteria comparing the energy distributions toa catalogue of distributions related to known medical conditions.
 3. Aprocedure used for extracting murmur information, characterised in thatit comprises the following steps: obtaining a digital representation ofheart sound for a predetermined number of seconds, identifying the timeof occurence of the first and second heart sounds in each cycle,windowing the parts of heart sounds falling between the first and secondheart sounds, and second and first heart sounds, respectivelydecomposition of the signals into a predetermined first number n1 offrequency bands, each band being decomposed into a predetermined secondnumber n2 of time-slices obtaining a systole (SP) and a diastole (DP)power vector consisting of the sum of n1 powers measured in each of then2 time slices for each combination of a frequency band and a timeslice, the power values from the different systoles are compared, andthe median value is chosen to be the standard value for a power vectorobtaining a systole (SMF) and a diastole (DMF) mean frequency vector byweighting the power value for each of n1 frequency bands with the meanfrequency of the corresponding band, summing the results and dividingthe sum by the corresponding element in the respective systole ordiastole power vector while using the time of occurence of the intensityvectors of the various classes for classifying the time distribution ofmurmurs.
 4. A procedure for extracting murmur information according toclaim 3, characterised in that a step preceding the step of obtainingsystole and diastole murmur intensity vectors SI and DI consists ofrefining the windowing by setting the values of SP, DP, SMF, and DMF ofthe first or last elements equal to the second or last-but-one values,respectively, if the values of the first or last elements of thecorresponding vectors fulfil predetermined deviation criteria.
 5. Aprocedure according to claim 3, characterised in that further steps areincluded in the procedure, comprising subjecting the signal to doubledifferentiation before decomposition obtaining a systole (SI) anddiastole (DI) murmur intensity vector, respectively, by taking thelogarithm of the corresponding SP and DP vectors, classifying theobtained logarithmic vectors into murmur intensity classes comparing theenergy distributions to a catalogue of distributions related to knownmedical conditions.
 6. An apparatus for performing the procedureaccording to claim 1, characterised in that it comprisesanalog-to-digital means for converting a heart sound signal into sampleddata, means for extracting the first and second heart sounds byclassification according to energy levels, means for eliminating thecontribution of the said first and second heart sounds from the signal,means for performing spectral analysis, means for performing featureenhancement, and multiplication means for obtaining the energy contentpresent in areas of a time-frequency representation delimited byfrequency band multiplied by time interval in the form of energydistributions means for classifying the energy distributions accordingto pre-defined criteria, and comparator means for comparing the energydistributions to a catalogue of distributions related to known medicalconditions.
 7. An apparatus for performing the procedure according toclaim 1, wherein signal processing means are used to produce a spatialsound distribution based on frequency, a low frequency band beingdelivered to a first earpiece of a headphone and a high frequency bandbeing delivered to a second earpiece of said headphone, the frequencybands containing first and second heart sounds and murmur soundsrespectively.
 8. An apparatus according to claim 7, wherein said signalprocessing means produce a temporal sound distribution, sound signalsbeing first delivered to a first earpiece of the headphone and thenbeing delivered to a second earpiece of the headphone.
 9. A apparatusaccording to claim 7 or 8, wherein said signal processing means compriseat least one Wiener filter.